Parutagouda S Khanagoudar,
Sushma B S,
Chandana C N,
Manikanta N,
Spoorti Suresh Awatimath,
- Professor, Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology Bangalore India
- Assistant Professor Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore
- Student Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology Bangalore India
- Student Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology Bangalore India
- Student Department of Data Science and Engineering, Nagarjuna College of Engineering and Technology Bangalore India
Abstract
India’s economy is based primarily on agriculture, as over 50% of the country’s population depends on it for their livelihood. The long-term viability of agriculture is seriously threatened by variations in the weather, climate, and other environmental factors. Because machine learning provides tools for decision assistance in agricultural yield prediction, including guidance on which crops to plant and when to plant them during the growing season, it is essential to the process. I’ll see to it. The goal of this work is to extract and synthesise features that are used to predict agricultural productivity through a systematic review. Furthermore, a number of techniques have been created to examine crop yield prediction through the use of machine learning algorithms. The decline in crop yield forecast efficiency and the decrease in relative error are neural networks’ primary drawbacks. Similarly, picking fruit for sorting and classification proved difficult due to supervised learning methods’ inability to capture the non-linear connection between input and output variables. In order to produce precise and effective models for crop classification, several studies have been suggested for the advancement of agriculture. This research includes estimating crop yields based on weather, plant diseases, crop classification based on growth stage, and more. This paper investigates many machine learning (ML) approaches used to agricultural production prediction and offers a thorough evaluation of the approaches’ precision
Keywords: Decision Tree Regressor, KNN Regressor, Random Forest Regressor, Linear Regressorc
[This article belongs to International Journal of Cheminformatics(ijci)]
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| Volume | 01 |
| Issue | 02 |
| Received | May 10, 2024 |
| Accepted | May 14, 2024 |
| Published | July 20, 2024 |